Machine Learning Engineering on AWS -3 Days- Authorised Instructor Led

In this power-packed course, you'll master the core fundamentals of machine learning and unlock the full potential of AWS to build intelligent, scalable solutions. Learn how to transform raw data into powerful insights, choose the right algorithms for any problem, and design end-to-end ML pipelines that scale effortlessly. Dive into CI/CD automation for ML workflows, apply top-tier security practices, and monitor deployed models with precision—detecting data drift before it impacts performance. Whether you're building your first model or scaling enterprise AI, this course will equip you with the skills to innovate with confidence. Enroll now and become the ML expert your organization needs!

This course includes presentations, hands-on labs, demonstrations, and group exercises.

In this course, you will learn to do the following:

• Explain ML fundamentals and its applications in the AWS Cloud.

• Process, transform, and engineer data for ML tasks by using AWS services.

• Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.

• Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration. • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows. • Discuss appropriate security measures for ML resources on AWS. • Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.

This course is designed for professionals who are interested in building, deploying, and operationalizing machine learning models on AWS. This could include current and in-training machine learning engineers who might have little prior experience with AWS. Other roles that can benefit from this training are DevOps engineer, developer, and SysOps engineer.

Prerequisites We recommend that attendees of this course have the following:

• Familiarity with basic machine learning concepts

• Working knowledge of Python programming language and common data science libraries

such as NumPy, Pandas, and Scikit-learn

• Basic understanding of cloud computing concepts and familiarity with AWS

• Experience with version control systems such as Git (beneficial but not required)

Day 1 Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS

Topic A: Introduction to ML

Topic B: Amazon SageMaker AI Topic C: Responsible ML Module 2: Analyzing Machine Learning (ML) Challenges Topic A: Evaluating ML business challenges Topic B: ML training approaches Topic C: ML training algorithms Module 3: Data Processing for Machine Learning (ML) Topic A: Data preparation and types Topic B: Exploratory data analysis Topic C: AWS storage options and choosing storage Module 4: Data Transformation and Feature Engineering Topic A: Handling incorrect, duplicated, and missing data Topic B: Feature engineering concepts Topic C: Feature selection techniques Topic D: AWS data transformation services Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK Day 2 Module 5: Choosing a Modeling Approach Topic A: Amazon SageMaker AI built-in algorithms Topic B: Amazon SageMaker Autopilot Topic C: Selecting built-in training algorithms Topic D: Model selection considerations Topic E: ML cost considerations **Module 6: Training Machine Learning (ML) Models ** Topic A: Model training concepts Topic B: Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI Module 7: Evaluating and Tuning Machine Learning (ML) models Topic A: Evaluating model performance Topic B: Techniques to reduce training time Topic C: Hyperparameter tuning techniques Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI Module 8: Model Deployment Strategies Topic A: Deployment considerations and target options Topic B: Deployment strategies Topic C: Choosing a model inference strategy Topic D: Container and instance types for inference Lab 5: Shifting Traffic Day 3 Module 9: Securing AWS Machine Learning (ML) Resources Topic A: Access control Topic B: Network access controls for ML resources Topic C: Security considerations for CI/CD pipelines Module 10: Machine Learning Operations (MLOps) and Automated Deployment Topic A: Introduction to MLOps Topic B: Automating testing in CI/CD pipelines Topic C: Continuous delivery services Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio Module 11: Monitoring Model Performance and Data Quality Topic A: Detecting drift in ML models Topic B: SageMaker Model Monitor Topic C: Monitoring for data quality and model quality